We follow the previous work [1]} to select 25 tasks (summarized in Appendix Table REF ) that can be grouped into few categories based on the task domain.
(i) standard datasets such as ImageNet-1K, Places205 and VOC07 which haven been widely used for testing features quality in many previous works [2]}, [3]}, [4]}, [5]}, [6]};
(ii) medical and satellite images such as in RESISC45 [7]}, EuroSAT [8]}, PatchCamelyon [9]};
(iii) structured datasets containing synthetic images and we select these datasets as even the best ImagNet representations fail to capture the aspects in these datasets such as counting and depth prediction tasks on CLEVR [10]} which contains simple 3D shapes with two tasks, camera-elevation prediction on SmallNorb [11]} which has images of artificial objects viewed under varying conditions, location
and orientation prediction tasks on dSprites [12]} which contain 2D rendered black/white shapes;
(iv) activity recognition in videos by taking the middle frame on datasets Kinetics-700 [13]} and UCF-101 [14]} and scene recognition on SUN397 [15]};
(v) self-driving related tasks such as german traffic sign recognition in GTSRB [16]}, measuring the distance of nearest vehicle in KITTI-Distance [17]};
(vi) textures on datasets such as DTD [18]};
(vii) natural datasets such as STL-10 [19]}, Oxford-IIT Pets [20]}, Oxford Flowers102 [21]}, Caltech-101 [22]} and finally
(viii) optical character recognition (OCR) tasks such as on SVHN [23]} which involves street number transcription on the distribution of Google Street View photos.
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